Rare mutations in the surfactant protein-B gene (SFTPB) and other genes in the pulmonary surfactant metabolic network cause lethal neonatal respiratory distress syndrome in human newborn infants by disrupting metabolism and function of the pulmonary surfactant. Mutation frequencies (<1-2%) in SFTPB and 2 other candidate genes in the pulmonary surfactant network (SFTPC and ABCA3) do not account for heritability of neonatal respiratory distress syndrome (h2~0.2-0.8) suggested by twin studies. To develop a comprehensive catalogue of genes and gene networks that account for the heritability of this complex disease, we propose to test the hypothesis that excess, rare, functionally disruptive single nucleotide polymorphisms (SNPs) characterize genes and gene networks associated with increased risk of neonatal respiratory distress syndrome. Specifically, using an agnostic candidate gene identification algorithm and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Database, we will select a comprehensive list of genes (~2,000) and their cognate gene networks expressed in human lung. Next, to identify a subgroup of genes (N=250) and gene networks with excess, rare, disruptive exonic SNPs associated with neonatal respiratory distress syndrome, we will use hybridization selection/exon capture, next-generation sequencing technology, and new statistical strategies (e.g., Combined Multivariate and Collapsing (CMC) method) in a case-control cohort (N=250 cases, 250 controls) sized to provide adequate statistical power (>0.8). Finally, to validate the highest risk genes and to optimize statistical power to search for epistatic and network x network interactions that confer disease risk, we will use CMC, Bayesian, and logic tree methods to analyze exonic SNPs in a second case-control cohort (N=100 cases, 100 controls) also sized to provide adequate statistical power (>0.8). Using next-generation sequencing technology and state of the art statistical methods to elucidate the genetic complexity of neonatal respiratory distress syndrome will permit the development of personalized diagnostic tools and preventive therapeutic strategies that target whole gene networks rather than individual genes.